Enhance Drug Safety Research Articles

Assessment of potential drug-drug interactions in hospitalized patients with infectious diseases: an experience from a secondary care hospital

Background Polypharmacy is common among hospitalized patients with infectious infections owing to comorbidities or concomitant illnesses. This raises the likelihood of drug-drug interactions and creates uncertainty for healthcare providers. This study aimed to assess the potential drug-drug interactions (pDDIs) among hospitalized patients with infectious diseases in a secondary care hospital. Methods A prospective observational study was conducted in the internal medicine ward for six months after the ethics committee’s approval. Data were collected from patient case records, and prescriptions were screened for pDDIs from a portable electronic physician information database (PEPID) resource analyzed using SPSS, version 27.0. Results In total, 148 patient case records were analyzed, and 549 pDDIs were identified, with 66.8% having at least one or more DDIs. The mean number of drug interactions was 3.70 ± 4.58 per prescription. The most frequently encountered drug interactions were drug combinations such as bisoprolol with atorvastatin and aspirin with tazobactam/piperacillin. Bivariate analysis showed that age, comorbidities, length of hospital stay, and the number of drugs prescribed were risk factors associated with DDIs (p<0.05). In the multiple binary logistic regression analysis, DDIs were significantly associated with comorbidities and the number of prescribed medications (p<0.0001). Conclusions This study observed the prevalence of DDIs in hospitalized patients with infectious diseases of ‘moderate’ severity. Prescription screening using a drug information database assists in early identification and prevention of DDIs, enhancing drug safety and quality of patient-centered care.

Using Multiple Drug Similarity Networks to Promote Adverse Drug Event Detection

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.

Enhancing Herbal Efficacy: Synthesis and Evaluation of Nanosuspension from Withania somnifera Root Extract

Background: Ayurveda emphasizes the balance of mind, body, and spirit, drawing on ancient texts that highlight the medicinal properties of herbs like Withania somnifera, known for its wide range of therapeutic applications and pharmacological benefits. Commonly referred to as Indian ginseng or Ashwagandha, Withania somnifera, demonstrates a variety of therapeutic effect , including anti-inflammatory, antitumor, sedative, hypnotic, deobstruent effects, narcotic, antioxidant, tonic, antistress, diuretic, aphrodisiac, immunomodulatory, and rejuvenating properties. Nanotechnology, particularly through the development of nanosuspensions, offers a significant advancement in medicine, addressing challenges like poor solubility and low bioavailability of lipophilic drugs. Nanosuspensions can enhance drug safety and efficacy by improving solubility and altering pharmacokinetics. Methods: This study focuses on formulating and characterizing a nanosuspension using an ethanolic extract of Withania somnifera roots. The nanosuspension (NSWS) underwent comprehensive characterization, utilizing techniques such as FT-IR spectroscopy, particle size analysis, zeta potential measurement, polydispersity index (PDI) assessment, Field emission scanning electron microscopy (FESEM), X-ray diffraction analysis, pH measurement, and stability testing. This study seeks to explore the potential of nanosuspensions, aiming to integrate nanotechnology with herbal medicine to improve the safety and efficacy of herbal formulations. Results: The nanosuspension (NSWS) shown particle size about 133.09 nm, and its uniform particle distribution was indicated by a Polydispersity Index (PDI) about 0.27. Stability and uniformity were further supported by the zeta potential, particle size, and PDI values. Conclusion: Nanosuspension formulations represents a versatile strategy to improve the therapeutic efficacy of hydrophobic drugs across various routes of administration. Keywords: Withania somnifera, Nanosuspension, Characterization, FT-IR, PDI, FESEM

Preparation and evaluation of novel oral tacrolimus nanocochleates for organ transplantation to reduce individual differences and improve drug safety

After organ transplantation, patients require treatment with immunosuppressive drugs to prevent immune rejection and transplantation failure. Tacrolimus (FK506) is a widely used immunosuppressant known for its potent immunosuppressive effect and narrow therapeutic range. Monitoring of FK506 blood concentrations is essential to avoid nephrotoxicity. In this study, a novel FK506 nanomedicine (FK506 cochleates) was developed using a microfluidic method to reduce variability among individuals and improve drug safety. The particle size of FK506 cochleates was (183.3 ± 1.4) nm, the zeta potential was −(39.28 ± 2.12) mV, and the encapsulation efficiency was more than 85 %. Particle size of FK506 cochleates could be maintained for up to 12 weeks in freeze-dried powder form. Small-angle X-ray scattering (SAXS) experiment confirmed the formation of cochleates by adding calcium solution. In vitro release studies demonstrated a sustained-release profile of FK506 from the cochleates carrier. Furthermore, the cochleates carrier could protect FK506 from the influence of stomach acid and slowly release the drug in the intestine. After oral administration, FK506 cochleates exhibited sustained-release properties in rats, accumulating in the spleen and lymph nodes − key anatomical sites for FK506’s pharmacological action. Importantly, FK506 cochleates significantly prolonged the survival time in the rabbit heart transplantation model while maintaining good safety profiles. In conclusion, the FK506 cochleates showed promising potential for enhancing drug safety in therapeutic organ transplantation.

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry.

Design, synthesis, evaluation, pharmacophore modeling, and 3D-QSAR of lappaconitine analogs as potential analgesic agents.

Alleviating pain is crucial for patients with various diseases. This study aimed to enhance the analgesic properties of lappaconitine, a natural drug, through structural modifications. Specifically, carbamate analgesic active fragments were innovatively introduced at multiple sites on the benzene ring of lappaconitine. A total of 53 lappaconitine analogs were synthesized and evaluated. Compounds 5a, 5c, 5e, 6, and 15j addressed the narrow therapeutic window of lappaconitine, enhancing drug safety. Notably, carbamate analogs exhibited significantly enhanced analgesic activity, with compounds 5a and 5c having ED50 values of 1.2 and 1.6 mg/kg, respectively, indicating higher potency than lappaconitine (3.5 mg/kg). A metabolic analysis of compound 5e was conducted in mice, revealing its primary metabolic processes and metabolites, and providing preliminary exploration for the druggability. Given the multiple analgesic targets of lappaconitine, its analgesic mechanism remains inconclusive. This study, for the first time, analyzed the pharmacological activity characteristics of the lappaconitine analogs using a pharmacophore model and established a three-dimensional quantitative structure-activity relationship (3D-QSAR) to elucidate the quantitative relationship between the structures of the synthesized compounds and their analgesic activities. These findings provide valuable guidance for future structural modification and optimization of analgesic drugs.

Innovations in Pharmacovigilance: Leveraging Artificial Intelligence for Enhanced Drug Safety Monitoring

The technique of pharmacovigilance, which involves keeping an eye on how medical pharmaceuticals are working after they've been approved for use, is vital in making sure that drugs are safe to use. Novel approaches brought about by the advent of Artificial Intelligence (AI) have the potential to greatly improve pharmacovigilance by making ADR identification, evaluation, and prevention much more effective. This study delves into the most recent advancements in AI-driven pharmacovigilance, shedding light on how drug safety monitoring is being revolutionised by machine learning algorithms, natural language processing, and big data analytics. In this article, we will go over the pros, cons, and possible next steps for pharmacovigilance frameworks that use AI.

Effect of Levobupivacaine Hydrochloride-Loaded Nanospheres on Delayed Cerebral Vasospasm Following Subarachnoid Hemorrhage in Rabbits

This research was aimed to analyze the mechanism of action of levobupivacaine hydrochloride-loaded nanospheres on delayed cerebral vasospasm following subarachnoid hemorrhage (SAH). Levobupivacaine hydrochloride-loaded nanospheres (LevoBPV Hcl/PLGA) were prepared using the solvent evaporation methodology, with the raw material as a control. The blood drug concentrations were detected by HPLC after subcutaneous and subarachnoid administration in experimental rabbits. Forty New Zealand white rabbits were randomly assigned into Sham group, SAH group, LevoBPV Hcl group (10 mg/kg), and LevoBPV Hcl/PLGA group (10 mg/kg), with 10 rabbits in each group. The SAH model was induced using the double blood injection methodology combined with internal carotid artery ligation. Brain tissue samples were collected on day 7 for pathological characterization, determination of neuronal apoptosis, and measurement of basilar artery diameter and area. The levels of oxidative stress factors (superoxide (SOD), malondiadehyde (MDA), glutathione peroxidase (GSH-Px)) and vasoconstrictor factors (nitric oxide (NO), endothelin-1 (ET-1)) in the cerebrospinal fluid (CSF) were detected using assay kits. The results revealed that the drug loading capacity of LevoBPV Hcl/PLGA was 29.13%, encapsulation efficiency was 87.09%, and the average particle size was 81.43 μm. Under the same dosage, both subcutaneous and subarachnoid administration of LevoBPV Hcl/PLGA exhibited two concentration peaks in the blood drug concentration, with lower concentration values versus LevoBPV Hcl group, and a longer average residence time than LevoBPV Hcl group (P < 0.05). Relative to Sham group, SAH group exhibited decreased diameter and area of the basilar artery, reduced neuronal density, increased neuronal apoptosis rate, decreased levels of SOD, GSH-Px, and NO in the CSF, and increased levels of MDA and ET-1 (P < 0.05). Moreover, LevoBPV Hcl group and LevoBPV Hcl/PLGA group showed increased diameter and area of the basilar artery, higher neuronal density, reduced neuronal apoptosis rate, elevated levels of SOD, GSH-Px, and NO in the CSF, and decreased levels of MDA and ET-1 versus SAH group (P < 0.05). The LevoBPV Hcl/PLGA group exhibited increased diameter and area of the basilar artery, higher neuronal density, reduced neuronal apoptosis rate, elevated levels of SOD, GSH-Px, and NO in the CSF, and decreased levels of MDA and ET-1 versus LevoBPV Hcl group (P < 0.05). In short, LevoBPV HCl-loaded nanospheres can prolong the in vivo residence time of subcutaneous and subarachnoid administration, reduce the maximum blood drug concentration, and enhance drug safety. Furthermore, these nanospheres can inhibit neuronal apoptosis following SAH, regulate oxidative stress and vasoconstrictor factor expression, thereby suppressing the occurrence of delayed cerebral vasospasm and alleviating brain tissue damage.

Assessing Post-Marketing Requirements for Orphan Drugs: A Cross-Sectional Analysis of FDA and EMA Oversight.

The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) oversee pharmaceutical regulations, including orphan drugs targeting rare diseases with limited patient populations. Post-marketing studies are crucial for monitoring safety and efficacy, with post-marketing requirements (PMRs) mandated by the regulatory agencies to ensure compliance. This study aims to compare PMR statuses, objectives, and pivotal trial characteristics of orphan drugs approved by the FDA (n = 154) and EMA (n = 79) from 2008 to 2018, shedding light on regulatory differences and their impact on drug development. Contrary to expectations, our analysis found no significant disparity in the proportion of orphan drugs with and without PMRs approved by both the FDA (48.1%) and EMA (55.7%). Safety concerns surrounding orphan drugs post-approval, attributed partly to pivotal trial design, underscore the need for robust post-marketing surveillance. While the FDA primarily focuses on post-marketing safety (36.1%), the EMA places a higher emphasis on both efficacy and safety (47.1%), reflecting distinct approaches to PMR management between the two regulatory bodies. The observed trend of delayed PMRs at the EMA (47.1%) highlights the importance of effective cooperation between regulators and pharmaceutical companies to ensure the timely completion of PMRs and enhance drug safety.

Assessment of potential drug-drug interactions in hospitalized patients with infectious diseases: an experience from a secondary care hospital

Background Polypharmacy is common among hospitalized patients with infectious infections owing to comorbidities or concomitant illnesses. This raises the likelihood of drug-drug interactions and creates uncertainty for healthcare providers. This study aimed to assess the potential drug-drug interactions (pDDIs) among hospitalized patients with infectious diseases in a secondary care hospital. Methods A prospective observational study was conducted in the internal medicine ward for six months after the ethics committee’s approval. Data were collected from patient case records, and prescriptions were screened for pDDIs from a portable electronic physician information database (PEPID) resource analyzed using SPSS, version 27.0. Results In total, 148 patient case records were analyzed, and 549 pDDIs were identified, with 66.8% having at least one or more DDIs. The mean number of drug interactions was 3.70 ± 4.58 per prescription. The most frequently encountered drug interactions were drug combinations such as bisoprolol with atorvastatin and aspirin with tazobactam/piperacillin. Bivariate analysis showed that age, comorbidities, length of hospital stay, and the number of drugs prescribed were risk factors associated with DDIs (p<0.05). In the multiple binary logistic regression analysis, DDIs were significantly associated with comorbidities and the number of prescribed medications (p<0.0001). Conclusions This study observed the prevalence of DDIs in hospitalized patients with infectious diseases of ‘moderate’ severity. Prescription screening using a drug information database assists in early identification and prevention of DDIs, enhancing drug safety and quality of patient-centered care.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis.

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

A Systematic Review on Plant-Derived Extracellular Vesicles as Drug Delivery Systems.

This systematic review offers a comprehensive analysis of plant-derived extracellular vesicles (PDEVs) as emerging drug delivery systems, focusing on original research articles published between 2016 and 2024 that exclusively examine the use of PDEVs for drug delivery. After a rigorous search across multiple databases, 20 relevant studies out of 805 initial results were selected for analysis. This review systematically summarizes the critical data on PDEV components, isolation methods, and drug-loading techniques. It highlights the potential of PDEVs to significantly enhance drug safety and efficacy, reduce dosage and toxicity, and align drug development with sustainable and environmentally friendly biotechnological processes. This review also emphasizes the advantages of PDEVs over mammalian-derived vesicles, such as cost-effectiveness, higher yield, and reduced immunogenicity. Additionally, it explores the synergistic potential between encapsulated drugs and bioactive compounds naturally present in PDEVs. This study acknowledges the challenges in standardizing isolation and formulation methods for clinical use. Overall, this review provides valuable insights into the current state and future directions of PDEV-based drug delivery systems, highlighting their promising role in advancing pharmaceutical research and development.

Nanoparticle-Based Drug Delivery Systems for Inflammatory Bowel Disease Treatment.

Inflammatory bowel disease (IBD) is a chronic, non-specific inflammatory condition characterized by recurring inflammation of the intestinal mucosa. However, the existing IBD treatments are ineffective and have serious side effects. The etiology of IBD is multifactorial and encompasses immune, genetic, environmental, dietary, and microbial factors. The nanoparticles (NPs) developed based on specific targeting methodologies exhibit great potential as nanotechnology advances. Nanoparticles are defined as particles between 1 and 100 nm in size. Depending on their size and surface functionality, NPs exhibit different properties. A variety of nanoparticle types have been employed as drug carriers for the treatment of inflammatory bowel disease (IBD), with encouraging outcomes observed in experimental models. They increase the bioavailability of drugs and enable targeted drug delivery, promoting localized treatment and thus enhancing efficacy. Nevertheless, numerous challenges persist in the translation from nanomedicine to clinical application, including enhanced formulations and preparation techniques, enhanced drug safety profiles, and so forth. In the future, it will be necessary for scientists and clinicians to collaborate in order to study disease mechanisms, develop new drug delivery strategies, and screen new nanomedicines. Nevertheless, numerous challenges persist in the translation from nanomedicine to clinical application, including enhanced formulations and preparation techniques, enhanced drug safety profiles, and so forth. In the future, it will be necessary for scientists and clinicians to collaborate in order to study disease mechanisms, develop new drug delivery strategies, and screen new nanomedicines.

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction.

The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous graph representation learning have attracted widespread interest in DDI event prediction and have yielded satisfactory results, but there is still room for improvement in prediction performance. In this study, we proposed a meta-path-based heterogeneous graph contrastive learning model, MPHGCL-DDI, for DDI event prediction. The model constructs two contrastive views based on meta-paths: an average graph view and an augmented graph view. The former represents that there are connections between drugs, while the latter reveals how the drugs connect with each other. We defined three levels of data augmentation schemes in the augmented graph view and adopted a combination of three losses in the model training phase: multi-relation prediction loss, unsupervised contrastive loss and supervised contrastive loss. Furthermore, the model incorporates indirect drug information, protein-protein interactions (PPIs), to reveal latent relations of drugs. We evaluated MPHGCL-DDI on three different tasks of two datasets. Experimental results demonstrate that MPHGCL-DDI surpasses several state-of-the-art methods in performance.

Empowering Pharmacovigilance: The Role of Web 3.0 Technologies in Enhancing Drug Safety and Patient Engagement

The rise of Web 3.0 technology suggests excellent progress in many fields, like healthcare. In this study, we explored the possible impact that Web 3.0 could have on reducing significant drug side effects among patients, which is an ongoing problem in pharmacotherapy. We looked at how often and in what ways adverse reactions to medication occur by analyzing a full dataset containing all drugs' alternatives as well as their corresponding effects, uses and classifications. Our method includes examining data and knowledge about decentralized data handling, safety, and approaches that focus on users in Web 3.0. The results indicate how much better drug safety monitoring can be achieved through decentralized applications, smart contracts, and blockchain technology. It also shows the potential to make patient therapy more personal and motivate patient education and involvement in healthcare decisions. This study emphasizes the ability of Web 3.0 to transform the monitoring of drug safety and effectiveness; it presents a strong case for its use within healthcare systems. The ideas for doctors, patients and healthcare policymakers highlight the crucial need to include Web 3.0 technology to improve patient results and reduce drug-linked morbidity along with death rates.

Pharmacogenomics as a Tool in Addressing Genetic VariationDependent Adverse Drug Reactions

Adverse drug reactions (ADRs) are a cause of discontinuing drug development and withdrawal from market, as well as a very common source of morbidity and mortality. Genetic variables may be the primary predictor of drug response for certain medications, but they are estimated to account for 15– 30% of the variability in drug response. Many factors can contribute to adverse drug reactions, including genetics and drug targeting/delivery. Genetic markers, such as single nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes, drug targets, and human leukocyte antigen (HLA) genotypes, have been associated with an increased risk of ADRs. Pharmacogenomics is the study of the genetic variation in the way that different people react to different pharmaceuticals, including variations in the risk of adverse drug reactions, dose requirements, and efficacy. The implementation of genetic data for predicting responses to medications and ADRs is becoming a reality in clinical practice, offering the potential to reduce the incidence of ADRs and improve patient outcomes. As pharmacogenomic research continues to advance, it holds great promise for enhancing drug safety and efficacy, ultimately leading to more tailored and effective therapeutic interventions.

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model’s focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.

Pharmacovigilance in Nigeria: Addressing challenges in ensuring drug safety and monitoring adverse effects

This paper examines the specific challenges faced by Nigeria in implementing pharmacovigilance measures to ensure drug safety and monitor adverse effects. Despite the critical importance of pharmacovigilance in safeguarding public health, Nigeria grapples with several obstacles in its implementation. Through a nuanced exploration of literature and regulatory frameworks, this study sheds light on the unique hurdles encountered in pharmacovigilance practices within the Nigerian context. Challenges in Nigeria's pharmacovigilance landscape include limited awareness and understanding of pharmacovigilance principles among healthcare professionals and consumers, resulting in underreporting of adverse drug reactions. Additionally, there is a lack of robust infrastructure and resources for pharmacovigilance activities, including inadequate funding, shortage of trained personnel, and deficient pharmacovigilance databases. Regulatory enforcement and oversight are also hindered by issues such as weak regulatory frameworks, fragmented reporting systems, and suboptimal collaboration among regulatory agencies. Furthermore, Nigeria's pharmacovigilance efforts are compounded by factors such as the proliferation of counterfeit and substandard medicines, informal drug markets, and cultural beliefs influencing healthcare-seeking behaviors. These challenges pose significant barriers to effective pharmacovigilance implementation and compromise patient safety and public health outcomes in Nigeria. Addressing these challenges requires a multifaceted approach encompassing regulatory reforms, capacity building initiatives, public awareness campaigns, and collaboration with international partners. Strengthening regulatory frameworks, enhancing healthcare professional training on pharmacovigilance, establishing robust reporting systems, and fostering partnerships between regulatory agencies, healthcare institutions, and pharmaceutical companies are essential steps toward improving pharmacovigilance practices in Nigeria. Through a thorough examination of the Nigerian context, this paper aims to raise awareness of the specific challenges hindering pharmacovigilance implementation in the country and stimulate dialogue on potential solutions to enhance drug safety and monitoring of adverse effects in Nigeria's healthcare system.

An Antibody-Drug Conjugate for Multiple Myeloma Prepared by Multi-Arm Linkers.

First-line treatment of multiple myeloma, a prevalent blood cancer lacking a cure, using anti-CD38 daratumumab antibody and lenalidomide is often inadequate due to relapse and severe side effects. To enhance drug safety and efficacy, an antibody-drug conjugate, TE-1146, comprising six lenalidomide drug molecules site-specifically conjugated to a reconfigured daratumumab to deliver cytotoxic lenalidomide to tumor cells is developed. TE-1146 is prepared using the HighDAR platform, which employs i) a maleimide-containing "multi-arm linker" to conjugate multiple drug molecules creating a drug bundle, and ii) a designed peptide with a Zn2+-binding cysteine at the C-termini of a reconfigured daratumumab for site-specific drug bundle conjugation. It is shown that TE-1146 remains intact and effectively enters CD38-expressing tumor cells, releasing lenalidomide, leading to enhanced cell-killing effects compared to lenalidomide/daratumumab alone or their combination. This reveals the remarkable potency of lenalidomide once internalized by myeloma cells. TE-1146 precisely delivers lenalidomide to target CD38-overexpressing tumor cells. In contrast, lenalidomide without daratumumab cannot easily enter cells, whereas daratumumab without lenalidomide relies on Fc-dependent effector functions to kill tumor cells.

Assessment of potential drug-drug interactions in hospitalized patients with infectious diseases: an experience from a secondary care hospital

Background Polypharmacy is common among hospitalized patients with infectious infections owing to comorbidities or concomitant illnesses. This raises the likelihood of drug-drug interactions and creates uncertainty for healthcare providers. This study aimed to assess the potential drug-drug interactions (pDDIs) among hospitalized patients with infectious diseases in a secondary care hospital. Methods A prospective observational study was conducted in the internal medicine ward for six months after the ethics committee’s approval. Data were collected from patient case records, and prescriptions were screened for pDDIs from a portable electronic physician information database (PEPID) resource analyzed using SPSS, version 27.0. Results In total, 148 patient case records were analyzed, and 549 pDDIs were identified, with 66.8% having at least one or more DDIs. The mean number of drug interactions was 3.70 ± 4.58 per prescription. The most frequently encountered drug interactions were drug combinations such as bisoprolol with atorvastatin and aspirin with tazobactam/piperacillin. Bivariate analysis showed that age, comorbidities, length of hospital stay, and the number of drugs prescribed were risk factors associated with DDIs (p<0.05). In the multiple binary logistic regression analysis, DDIs were significantly associated with comorbidities and the number of prescribed medications (p<0.0001). Conclusions This study observed the prevalence of DDIs in hospitalized patients with infectious diseases of ‘moderate’ severity. Prescription screening using a drug information database assists in early identification and prevention of DDIs, enhancing drug safety and quality of patient-centered care.